Towards Energy- and QoS-aware Load Balancing for 5G Advanced: Leveraging O-RAN to Achieve Sustainability and Energy Efficiency

Abstract

The increasing energy consumption of next-generation mobile networks necessitates the adoption of autonomous and energy-aware management strategies. This article proposes a novel adaptive solution leveraging the O-RAN architecture to optimize energy efficiency while managing its trade-off with QoS. The proposed approach introduces a hierarchical O-RAN-aligned control framework in which a Non-RT RIC periodically computes energy-aware policies from long-term historical data, while a Near-RT RIC enforces those policies through per-UE handover actions on a sub-second timescale. We formulate a joint energy- and QoS-aware load balancing problem as a MINLP model. This model optimizes UE association across O-RUs to minimize transmission power and autonomously deactivate underutilized cells, while enforcing per-UE throughput requirements as explicit constraints at each optimization cycle. To validate the proposed solution, we deploy it in an experimental environment that simulates massive sports events. Experimental results demonstrate 72% energy savings over a 24-hour trace and sub-second handover delays (0.123-0.204 s/UE), confirming the solution's feasibility for autonomous and sustainable 5G Advanced networks. An offline physical-layer throughput evaluation further characterizes the trade-off between energy efficiency and QoS, establishing the operational limits of dynamic cell deactivations. This work provides guidelines for deploying energy-efficient strategies in O-RAN environments and underscores the potential of adaptive solutions for sustainable mobile communications.

16 pages, 13 figures, 6 tables